Oxana Tchepel

Impact of road traffic emissions on air quality of the Lisbon region

The main purpose of this paper is to present the study of traffic emissions impact on the Lisbon region air quality. Two approaches of emission data generation with high spatial and temporal resolution are presented and compared. Main roads were processed as line sources and hot on-road emissions were calculated based on daily mean traffic and emission factors distinguished for several road classes and vehicle types. Also, the disaggregation of national CORINAIR inventory has been performed on the basis of statistical information of fuel consumption and population density. The comparison of emission data obtained by these two approaches demonstrates a good agreement for total values, but a significant difference for spatial distribution of the data. To ensure completeness of the data, to improve their spatial resolution and also to analyse the impact of the traffic emissions, a combination of the two approaches was applied to generate the emission data used by a photochemical numerical system to simulate the atmospheric circulation and the air pollution pattern in Lisbon under summer meteorological conditions, having different emission scenarios. It was possible to conclude that an air pollution abatement strategy is urgently needed and it should take into account the strong contribution of road traffic emissions to the Lisbon air pollution levels.

Assessment of potential improvements on regional air quality modelling related with implementation of a detailed methodology for traffic emission estimation

The accuracy and precision of air quality models are usually associated with the emission inventories. Thus, in order to assess if there are any improvements on air quality regional simulations using detailed methodology of road traffic emission estimation, a regional air quality modelling system was applied. For this purpose, a combination of top-down and bottom-up approaches was used to build an emission inventory. To estimate the road traffic emissions, the bottom-up approach was applied using an instantaneous emission model (Vehicle Specific Power - VSP methodology), and an average emission model (CORINAIR methodology), while for the remaining activity sectors the top-down approach was used. Weather Research and Forecasting (WRF) and Comprehensive Air quality (CAMx) models were selected to assess two emission scenarios: (i) scenario 1, which includes the emissions from the top-down approach; and (ii) scenario 2, which includes the emissions resulting from integration of top-down and bottom-up approaches. The results show higher emission values for PM10, NOx and HC, for scenario 1, and an inverse behaviour to CO. The highest differences between these scenarios were observed for PM10 and HC, about 55% and 75% higher (respectively for each pollutant) than emissions provided by scenario 2. This scenario gives better results for PM10, CO and O3. For NO2 concentrations better results were obtained with scenario 1. Thus, the results obtained suggest that with the combination of the top-down and bottom-up approaches to emission estimation several improvements in the air quality results can be achieved, mainly for PM10, CO and O3.

Particulate matter and health risk under a changing climate: assessment for Portugal

The potential impacts of climate-induced changes in air pollution levels and its impacts on population health were investigated. The IPCC scenario (SRES A2) was used to analyse the effects of climate on future PM10 concentrations over Portugal and their impact on short-term population exposure and mortality. The air quality modelling system has been applied with high spatial resolution looking on climate changes at regional scale. To quantify health impacts related to air pollution changes, the WHO methodology for health impact assessment was implemented. The results point to 8% increase of premature mortality attributed to future PM10 levels in Portugal. The pollution episodes with daily average PM10 concentration above the current legislated value (50 μg·m−3) would be responsible for 81% of attributable cases. The absolute number of deaths attributable to PM10 under future climate emphasizes the importance of indirect effects of climate change on human health.

Quantification of health benefits related with reduction of atmospheric PM10 levels: implementation of population mobility approach

This study is focused on the assessment of potential health benefits by meeting the air quality limit values (2008/50/CE) for short-term PM10 exposure. For this purpose, the methodology of the WHO for Health Impact Assessment and APHEIS guidelines for data collection were applied to Porto Metropolitan Area, Portugal. Additionally, an improved methodology using population mobility data is proposed in this work to analyse number of persons exposed. In order to obtain representative background concentrations, an innovative approach to process air quality time series was implemented. The results provide the number of attributable cases prevented annually by reducing PM10 concentration. An intercomparison of two approaches to process input data for the health risk analysis provides information on sensitivity of the applied methodology. The findings highlight the importance of taking into account spatial variability of the air pollution levels and population mobility in the health impact assessment.

Emission modelling of hazardous air pollutants from road transport at urban scale

Abstract This study is focused on the development of a modelling approach to quantify emissions of traffic-related hazardous air pollutants in urban areas considering complex road network and detailed data on transport activity. In this work a new version of the Transport Emission Model for line sources has been developed for hazardous pollutants (TREM-HAP). Emission factors for benzene, 1,3-butadiene, formaldehyde, acetaldehyde, acrolein, naphthalene and also particulate matter (PM2.5) were implemented and the model was extended to integrate a probabilistic approach for the uncertainty quantification using Monte-Carlo technique. The methodology has been applied to estimate road traffic emissions in Porto Urban Area, Portugal. Hourly traffic counts provided by an automatic counting system were used to characterise the spatial and temporal variability of the number of vehicles, vehicle categories and average speed at different road segments. The data for two summer and two winter months were processed to o...

Influence of Traffic Emissions Estimation Variability on Urban Air Quality Modelling

The main objective of this work is to analyse how uncertainties in emission data of nitrogen oxides (NOx) and volatile organic compounds (VOC), originated from road traffic, influence the model prediction of ozone (O3) concentration fields. Different methods to estimate emissions were applied and results were compared in order to obtain their variability. Based on these data, different emission scenarios were compiled for each pollutant considering the minimum and the maximum values of the estimated emission range. These scenarios were used as input to the MAR-IV mesoscale modelling system. Simulations have been performed for a summer day in the Northern Region of Portugal. The different approaches to estimate NOx and VOC traffic emissions show a significant variability of absolute values and of their spatial distribution. Comparison of modelling results obtained from the two scenarios presents a dissimilarity of 37% for ozone concentration fields as a response of the system to a variation in the input emission data of 63% for NOx and 59% for VOC. Far beyond all difficulties and approximations, the developed methodology to build up an emission data base shows to be consistent and an useful tool in order to turn applicable an air quality model. Nevertheless, the sensitivity of the model to input data should be considered when it is used as a decision support tool.

Integrated modelling approach for the evaluation of low emission zones.

Low emission zones (LEZ) are areas where the most polluting vehicles are restricted or deterred from entering. In recent years, LEZ became a popular option to reduce traffic-related air pollution and have been implemented in many cities worldwide, notably in Europe. However, the evidence about their effectiveness is inconsistent. This calls for the development of tools to evaluate ex-ante the air quality impacts of a LEZ. The integrated modelling approach we propose in this paper aims to respond to this call. It links a transportation model with an emissions model and an air quality model operating over a GIS-based platform. Through the application of the approach, it is possible to estimate the changes induced by the creation of a LEZ applied to private cars with respect to air pollution levels not only inside the LEZ, but also, more generally, in the city where it is located. The usefulness of the proposed approach was demonstrated for a case study involving the city of Coimbra (Portugal), where the creation of a LEZ is being sought to mitigate the air quality problems that its historic centre currently faces. The main result of this study was that PM10 and NO2 emissions from private cars would decrease significantly inside the LEZ (63% and 52%, respectively) but the improvement in air quality would be small and exceedances to the air pollution limits adopted in the European Union would not be fully avoided. In contrast, at city level, total emissions increase and a deterioration of air quality is expected to occur.

Modeling of Human Exposure to Benzene in Urban Environments

Urban areas characterized by high spatial and temporal variability in air pollution levels require implementation of comprehensive approaches to address exposure of individuals. The main objective of this study was to implement a quantitative assessment of individual exposure to benzene in urban environments. For this purpose, ExPOSITION model based on a global positioning system (GPS) tracking approach was applied to estimate individual exposure in different microenvironments. The current investigation provides an application example and validation of the modeling approach against personal and biological exposure measurements collected during the measurements campaign. The probabilistic approach using the Johnson system of distributions was implemented to characterize variability of indoor concentrations. The results obtained for daily average individual exposure to benzene corresponded to mean levels of 1.6 and 0.8–2.7 μg/m3 in terms of 5th–95th percentiles. Validation of the model results against several personal exposure samples collected for the selected individuals revealed a Pearson’s correlation coefficient of .66. This modeling approach explicitly addressed the temporal and spatial variability in the exposure and established a source–receptor relationship.

Ensemble Techniques to Improve Air Quality Assessment: Focus on O3 and PM

Five air quality models were applied over Portugal for July 2006 with an ensemble purpose. These models were used, with their own meteorology, parameterizations, boundary conditions and chemical mechanisms, but with the same emission data. The validation of the individual models and its ensemble for ozone (O3) and particulate matter was performed using monitoring data from 22 background stations over Portugal. After removing the bias from each model, different ensemble techniques were applied and compared. Besides the median, several weighted ensemble approaches were tested and intercompared: static (SLR) and dynamic (DLR) multiple linear regressions (using less-square optimization method) and the Bayesian Model Averaging (BMA) methodology. The goal of the comparison is to estimate to what extent the ensemble analysis is an improvement with respect to the single model results. The obtained results revealed that no one of the 4 tested ensembles clearly outperforms the others on the basis of statistical parameters and probabilistic analysis (reliability and resolution properties). Nevertheless, statistical results have shown that the application of the weights slightly improves ensemble performance when compared to those obtained from the median ensemble. The same statistical analysis together with the probabilistic measures demonstrates that the SLR and BMA methods are the best performers amongst the assessed methodologies.

Advanced Numerical Methods for Complex Environmental Models: Needs and Availability

The understanding of lakes physical dynamics is crucial to provide scientifically credible information foron lakes ecosystem management. We show how the combination of in-situ dataobservations, remote sensing observationsdata and three15 dimensional hydrodynamic (3D) numerical simulations is capable of deliveringresolving various spatio-temporal scales involved in lakes dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we presentdevelop a flexible framework forby incorporating DA into lakes three-dimensional3D hydrodynamic lake models. Using an Ensemble Kalman Filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in-situ and satellite remote sensing temperature data into a three-dimensional3Dl hydrodynamic 20 model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatio-temporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed for the constraintswith a goal of near real-time operational systems and near real-time operations (e.g. integration into meteolakes.ch). 25